2013 imca annual big data in insurance
DESCRIPTION
Big Data presentation at 2013 Insurance Marketing Communication Assn. annual conference made by Pat Saporito, SAPTRANSCRIPT
Insurance Marketing Communication Assn.
Annual Conference, 2013
Big Data in Insurance & Implications for
Marketing
Pat Saporito, CPCU, Sr. Director, Global COE for Business Intelligence
© 2012 SAP AG. All rights reserved. 2 Confidential
Agenda
Digital Evolution & Innovations
Big Data
Growing Insurance Needs & Implications
Challenges for Marketing, Communications & IT
How to Play Nicer Together
© 2012 SAP AG. All rights reserved. 3 Confidential
Digital Marketing Innovations
1:1 Marketing
Amazon.com,
Gamification/ Game Design
Angry Birds, CastleVille
Group/Collective Buying
• Groupon, Living Social
Social Networking
• Facebook, Linked In
Inbound Marketing
Retargeting, remarketing
Travelocity
Location Based Marketing
• Google Places
Video
• YouTube, Hulu
User Generated Content
• Facebook, Linked In, Twitter
Mobile Technologies
• Smartphones, iPads/tablets
© 2012 SAP AG. All rights reserved. 4 Confidential
Drive Better
Profit Margins
New
Strategies and
Business Models
Operational
Efficiencies
Value
Velocity
Volume Variety
Mobile
CRM Data
Planning
Opportunities Transactions
Customer
Sales Order
Things
Instant Messages
Demand
Inventory
Big Data Matters Potential to Provide Transformational Business Value
© 2012 SAP AG. All rights reserved. 5 Confidential
“Today in 64% of enterprises, fewer than 10% of decision makers
use BI”
Gap between End Users and Big Data Leading to a future ‘Flash Point’ between both
Source: Forrester Research, 2012 BI Maturity Survey and Pioneer self assessment
Projected Growth in End User
Enablement
50% by 2014
75% by 2020
Sustained Explosive Growth in
Data Volumes
80% Data Growth Year on Year
Analytics 3.0 │The Era of Impact
1.0 Traditional Analytics
Data Economy: Rapid Insights Providing Business Impact
Big Data 2.0
3.0
• Primarily descriptive
analytics and reporting
• Internally sourced,
relatively small,
structured data
• “Back room” teams
of analysts
• Internal decision
support
• Analytics integral to running the
business; strategic asset
• Rapid and agile insight delivery
• Analytical tools available at
point of decision
• Cultural evolution embeds
analytics into decision and
operational processes
• All businesses can create data-
based products and services
• Complex, large,
unstructured data sources
• New analytical and
computational capabilities
• “Data Scientists” emerge
• Online firms create data-
based products and
services
Today
2013 © IIA All Rights Reserved www.iianalytics.com
© 2012 SAP AG. All rights reserved. 7 Confidential
What Big Data Means at SAP Marketing
SAP Insight Driven Marketing Team
1.0 B Records on SAP Community Network
500M Transactions in our Mktg Intel Platform
800 Users of our Mktg Effectiveness Platform/200 reports/views
2.0B Behavior combinations based on event attendance/234 behaviors
27M companies/25M contacts
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Insurance Analytic Evolution Where are you today? Where do you need to be?
Pricing & Underwriting
Traditional Class Rated
Portfolio Analysis Household Analysis, Tier Rating Plans
Risk Based Pricing, Ad-hoc or On Demand Rate Reviews
Data Poor Quality, Silo’d, Inaccessible Data
Data Assembled Across Product Lines/Historical
Consistent Enterprise View Knowledge/ Data Mining
Atomic Detail Data Wisdom/ Predictive
Product Development
One Product Fits All
Unbundled Coverages Cafeteria/ Menu Approach
Customer & Profitability Driven
Marketing
Product Value Customer Segment Value
Customer Lifetime Value
Dynamic Value Management
Accounting & Finance
Unit focused claims mgmt.
Integrated, but reactive claims mgmt.
Driver based historical claims mgmt.
Driver based predictive claims mgmt.
Metrics Silo’d, Functional, Lagging Metrics
SBU-Strategic Objective linked, historical drivers
Strategic & Cross-SBU objective linked, predictive drivers
Integrated predictive models & metrics
Claims
Traditional Planning & Budgeting
Driver Based Planning & Budgeting
Integrated Planning Predictive Planning
Less Advanced More Advanced
Reactive Predictive
New information signals
:-) Brand
Sentiment
Higher NPS
360O
Customer View
Loyal Customers
Product
Recommendation
More Sales
Propensity to
Churn
Greater
Retention
Real-time Demand/
Supply Forecast
More Efficient
Predictive
Maintenance
Less Downtime
Fraud Detection
Lower Risk
Network
Optimization
Lower Cost
Insider Threats
Greater
Security
Risk Mitigation, Real-
time
Retain Market Value
Asset Tracking
Increase Productivity
Personalized Care
Loyal Customers
What signals are
you missing?
“
”
In 2011 the amount
of data surpassed
1.8 Zettabytes
90% of the data in
the world today has
been created in the
last two years
alone
IDC Digital Universe
Study Extracting
Value from Chaos
A changing relationship with
information
From mass
production to
mass
specialization
Personalized
Insights
Advanced Planning and Forecasting
Sensing and Responding
Predictive Modeling
Real-time Reporting and Analysis
“
”
Every product and
service will be
offered to us in
exactly the way we
need it, not how
manufacturers want
to deliver it.
A Demographic of One,
Michael S. Malone
Information Culture
Use information as a strategic
asset in decisions
Build and tell fact-based
stories
Maximize performance with
effective use of information
Connecting people
to data
“
”
The stone age was
marked by man's
clever use of crude
tools; the
information age, to
date, has been
marked by man's
crude use of clever
tools.
Anon
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Big Data: Analysis Tools
Variety of tools for analyzing big data; new end user tools
78% are using reports and dashboards
68% are exploring predictive analysis
(programmatically or analytic tools)
67% are exploring visualization tools
50% are using custom applications
The Challenge of Big Data Benchmarking Large-Scale Data Management, Ventana Research, January 2012
© 2012 SAP AG. All rights reserved. 13 Confidential
Standard Reports
Ad-hoc Reports
OLAP & Visualization
Dashboards & Scorecards
Exploration & Visualization
Value
Predictive Modeling
Traditional Business Intelligence
Big Data Analytics
Org
an
izati
on
al &
Co
mp
eti
tive I
mp
act
Moving from Traditional BI to Big Data Analytics The Analytical Tools Continuum
Self Service Sweet Spot
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Where‘s the Value? Spending More Time Analyzing vs. Acquiring Data
Source: SAP – ASUG Value Engineering Benchmark Study
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SAP Vision for Intelligent Data
Art of the Possible – Customer Success Stories
Intelligent
Data
Data Explosion
User Proliferation and Expectations
Align
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SAP BusinessObjects Business Intelligence Vision
Five Innovation Pillars
Mobile
First experience
for BI
Content to point
of impact
Expand to
untapped users
Extreme
Big data
Real-time
Predictive
BI Core
Core for
innovation
Complete BI
Suite
Continued
Leadership
Extendable
foundation
Creative
For IT and
Department
Fast time-to-
value
Connected to
the Enterprise
Visualization
Social
Capture the
decision
Opinion and
Facts
Leverage
the network
Text
analytics,
sentiment
analysis
Innovation without Disruption
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Analytics for the CMO
Aberdeen Best in Class PACE Framework
Source: Aberdeen Group www.aberdeengroup.com
“Analytics for the CMO: How Best in Class Marketers Use Customer Insights to Drive More Revenue”
Analytics
Marketing
Resource
Management
Customer &
Segment
Analysis
Multi-Channel
Engagement
Campaign
Management
Mobile
Inbound
Response
21st Century Marketing SAP Marketing Analytics Approach
Customer
Social
Monitoring &
Filtering
Loyalty &
Rewards
Real Time
Offers
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Challenges in Getting to Value
Marketing Issues with IT
• IT too slow is responding to
requests
• Have to go through IT to get data
• Data quality/confidence
• Data manipulation tools are
lacking
• Not responsive or innovative
enough
IT Issues with Marketing
• Too many requests
• Don’t know what they want
• Create additional data
islands/marts; more data to
manage
• Create more data ambiguity
• Never satisfied
© 2012 SAP AG. All rights reserved. 20 Confidential
Common Marketing Analytic Pains
Copyright SAP AG. Road to BI Success/BI Strategy Self Assessment Survey. www.sap.com/bistrategy/
© 2012 SAP AG. All rights reserved. 21 Confidential
Copyright SAP AG. Road to BI Success/BI Strategy Self Assessment Survey. www.sap.com/bistrategy/
Common Marketing Analytic Pains (Cont’d)
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Copyright SAP AG. Road to BI Success/BI Strategy Self Assessment Survey. www.sap.com/bistrategy/
Common IT Analytic Pains
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Self-Service BI Strategy Assessment
www.sap.com/bistrategy
A self-service online assessment tool that can help you identify business
challenges across your organization.
© 2012 SAP AG. All rights reserved. 24 Confidential
How to Play Nicer Together
• Marketing & IT alignment; IT needs to focus on business-driven BI
• Marketing defines business needs (describe strategic initiatives, current pains
and business value to address; build your business case)
• IT provides an “Innovation Sandbox”
• IT provisions data and database infrastructure
• IT helps identify/vet more self-service tools for Marketing
• Consider Cloud environment for your Sandbox or use “trial” programs
• Look outside the insurance industry for ideas/innovations
There’s a Data Maestro in all of us
www.sap.com/datachallenge
Join the SAP data
challenge
“
”
What story is your
Data telling?
Free download of SAP
Lumira (data exploration &
visualization tool)
www.sap.com/trylumira
Thank You!
Pat Saporito, CPCU
Sr. Director, BI
Global COE for Analytics
(201) 681-9671
Twitter: @Pat.Saporito
LinkedIn: www.linkedin/in/patriciasaporito
SAP Collaboration Network
http://scn.sap.com/
SAP Decision Factor Blog
http://www.the-decisionfactor.com/home/